Abstract
Electron energy-loss spectroscopy is a well-established technique for characterizing low-Z elements in materials. Typically, a measured spectrum image is contributed from several materials when the composition of the specimen is sophisticated. Therefore, decomposing the distribution of each endmember is crucial to material scientists. In this article, we combined multiple linear least-squares fitting and k-means clustering to resolve the aforementioned issue. In addition, our method can nearly extract the true endmembers in materials unsupervisedly. Simulated and experimental data were employed to evaluate the performance and feasibility of our method.
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